Simple follow-up to #13620. Modify `tsdb.PostingsForMatchers` to use the optimized tsdb.IndexReader.PostingsForLabelMatching method also for inverse matching.
Introduce method `PostingsForAllLabelValues`, to avoid changing the existing method.
The performance is much improved for a subset of the cases; there are up to
~60% CPU gains and ~12.5% reduction in memory usage.
Remove `TestReader_InversePostingsForMatcherHonorsContextCancel` since
`inversePostingsForMatcher` only passes `ctx` to `IndexReader` implementations now.
Signed-off-by: Arve Knudsen <arve.knudsen@gmail.com>
* tsdb: check for context cancel before regex matching postings
Regex matching can be heavy if the regex takes a lot of cycles to
evaluate and we can get stuck evaluating postings for a long time
without this fix. The constant checkContextEveryNIterations=100
may be changed later.
Signed-off-by: György Krajcsovits <gyorgy.krajcsovits@grafana.com>
This PR is a reference implementation of the proposal described in #10420.
In addition to what described in #10420, in this PR I've introduced labels.StableHash(). The idea is to offer an hashing function which doesn't change over time, and that's used by query sharding in order to get a stable behaviour over time. The implementation of labels.StableHash() is the hashing function used by Prometheus before stringlabels, and what's used by Grafana Mimir for query sharding (because built before stringlabels was a thing).
Follow up work
As mentioned in #10420, if this PR is accepted I'm also open to upload another foundamental piece used by Grafana Mimir query sharding to accelerate the query execution: an optional, configurable and fast in-memory cache for the series hashes.
Signed-off-by: Marco Pracucci <marco@pracucci.com>
Drop context argument from tsdb/index.Symbols.Lookup since lookup
should be fast and the context checking is a performance hit.
Signed-off-by: Arve Knudsen <arve.knudsen@gmail.com>
Instead of passing in a `ScratchBuilder` and `Labels`, just pass the
builder and the caller can extract labels from it. In many cases the
caller didn't use the Labels value anyway.
Now in `Labels.ScratchBuilder` we need a slightly different API: one
to assign what will be the result, instead of overwriting some other
`Labels`. This is safer and easier to reason about.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
This necessitates a change to the `tsdb.IndexReader` interface:
`index.Reader` is used from multiple goroutines concurrently, so we
can't have state in it.
We do retain a `ScratchBuilder` in `blockBaseSeriesSet` which is
iterator-like.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* Add BenchmarkOpenBlock
* Use specific types when reading offset table
Instead of reading a generic-ish []string, we can read a generic type
which would be specifically labels.Label.
This avoid allocating a slice that escapes to the heap, making it both
faster and more efficient in terms of memory management.
* Update error message for unexpected number of keys
* s/posting offset table/postings offset table/
* Remove useless lastKey assignment
* Use two []bytes vars, simplify
Applied PR feedback: removed generics, moved the label indices reading
to that specific test as we're not using it in production anyway, we're
just testing what we've just built.
Also using two []bytes variables for name and value that use the backing
buffer instead of using strings, this reduces allocations a lot as we
only copy them when we store them (this is optimized by the compiler).
* Fix the dumb bug
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
Co-authored-by: Marco Pracucci <marco@pracucci.com>
* refactor: move from io/ioutil to io and os packages
* use fs.DirEntry instead of os.FileInfo after os.ReadDir
Signed-off-by: MOREL Matthieu <matthieu.morel@cnp.fr>
Added validation to expected postings length compared to the bytes slice
length. With 32bit postings, we expect to have 4 bytes per each posting.
If the number doesn't add up, we know that the input data is not
compatible with our code (maybe it's cut, or padded with trash, or even
written in a different coded).
This is needed in downstream projects to correctly identify cached
postings written with an unknown codec, but it's also a good idea to
validate it here.
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
This creates a new `model` directory and moves all data-model related
packages over there:
exemplar labels relabel rulefmt textparse timestamp value
All the others are more or less utilities and have been moved to `util`:
gate logging modetimevfs pool runtime
Signed-off-by: beorn7 <beorn@grafana.com>
* TSDB: demistify seriesRefs and ChunkRefs
The TSDB package contains many types of series and chunk references,
all shrouded in uint types. Often the same uint value may
actually mean one of different types, in non-obvious ways.
This PR aims to clarify the code and help navigating to relevant docs,
usage, etc much quicker.
Concretely:
* Use appropriately named types and document their semantics and
relations.
* Make multiplexing and demuxing of types explicit
(on the boundaries between concrete implementations and generic
interfaces).
* Casting between different types should be free. None of the changes
should have any impact on how the code runs.
TODO: Implement BlockSeriesRef where appropriate (for a future PR)
Signed-off-by: Dieter Plaetinck <dieter@grafana.com>
* feedback
Signed-off-by: Dieter Plaetinck <dieter@grafana.com>
* agent: demistify seriesRefs and ChunkRefs
Signed-off-by: Dieter Plaetinck <dieter@grafana.com>
* Testify: move to require
Moving testify to require to fail tests early in case of errors.
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* More moves
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* Refactor test assertions
This pull request gets rid of assert.True where possible to use
fine-grained assertions.
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
Add back Windows CI, we lost it when tsdb was merged into the prometheus
repo. There's many tests failing outside tsdb, so only test tsdb for
now.
Fixes#6513
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
Rather than buffer up symbols in RAM, do it one by one
during compaction. Then use the reader's symbol handling
for symbol lookups during the rest of the index write.
There is some slowdown in compaction, due to having to look through a file
rather than a hash lookup. This is noise to the overall cost of compacting
series with thousands of samples though.
benchmark old ns/op new ns/op delta
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 539917175 675341565 +25.08%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 2441815993 2477453524 +1.46%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 3978543559 3922909687 -1.40%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 8430219716 8586610007 +1.86%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 1786424591 1909552782 +6.89%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 5328998202 6020839950 +12.98%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 10085059958 11085278690 +9.92%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 25497010155 27018079806 +5.97%
BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 2427391406 2817217987 +16.06%
BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 2592965497 2538805050 -2.09%
BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 2437388343 2668012858 +9.46%
BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 2317095324 2787423966 +20.30%
BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 2600239857 2096973860 -19.35%
benchmark old allocs new allocs delta
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 500851 470794 -6.00%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 821527 791451 -3.66%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 1141562 1111508 -2.63%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 2141576 2111504 -1.40%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 871466 841424 -3.45%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 1941428 1911415 -1.55%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 3071573 3041510 -0.98%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 6771648 6741509 -0.45%
BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 731493 824888 +12.77%
BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 793918 887311 +11.76%
BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 811842 905204 +11.50%
BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 832244 925081 +11.16%
BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 921553 1019162 +10.59%
benchmark old bytes new bytes delta
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 40532648 35698276 -11.93%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 60340216 53409568 -11.49%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 81087336 72065552 -11.13%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 142485576 120878544 -15.16%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 208661368 203831136 -2.31%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 347345904 340484696 -1.98%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 585185856 576244648 -1.53%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 1357641792 1358966528 +0.10%
BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 126486664 119666744 -5.39%
BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 122323192 115117224 -5.89%
BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 126404504 119469864 -5.49%
BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 119047832 112230408 -5.73%
BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 136576016 116634800 -14.60%
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
Rather than keeping the entire symbol table in memory, keep every nth
offset and walk from there to the entry we need. This ends up slightly
slower, ~360ms per 1M series returned from PostingsForMatchers which is
not much considering the rest of the CPU such a query would go on to
use.
Make LabelValues use the postings tables, rather than having
to do symbol lookups. Use yoloString, as PostingsForMatchers
doesn't need the strings to stick around and adjust the API
call to keep the Querier open until it's all marshalled.
Remove allocatedSymbols memory optimisation, we no longer keep all the
symbol strings in heap memory. Remove LabelValuesFor and LabelIndices,
they're dead code. Ensure we've still tests for label indices,
and add missing test that we can work with old V1 Format index files.
PostingForMatchers performance is slightly better, with a big drop in
allocation counts due to using yoloString for LabelValues:
benchmark old ns/op new ns/op delta
BenchmarkPostingsForMatchers/Block/n="1"-4 36698 36681 -0.05%
BenchmarkPostingsForMatchers/Block/n="1",j="foo"-4 522786 560887 +7.29%
BenchmarkPostingsForMatchers/Block/j="foo",n="1"-4 511652 537680 +5.09%
BenchmarkPostingsForMatchers/Block/n="1",j!="foo"-4 522102 564239 +8.07%
BenchmarkPostingsForMatchers/Block/i=~".*"-4 113689911 111795919 -1.67%
BenchmarkPostingsForMatchers/Block/i=~".+"-4 135825572 132871085 -2.18%
BenchmarkPostingsForMatchers/Block/i=~""-4 40782628 38038181 -6.73%
BenchmarkPostingsForMatchers/Block/i!=""-4 31267869 29194327 -6.63%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",j="foo"-4 112733329 111568823 -1.03%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",i!="2",j="foo"-4 112868153 111232029 -1.45%
BenchmarkPostingsForMatchers/Block/n="1",i!=""-4 31338257 29349446 -6.35%
BenchmarkPostingsForMatchers/Block/n="1",i!="",j="foo"-4 32054482 29972436 -6.50%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",j="foo"-4 136504654 133968442 -1.86%
BenchmarkPostingsForMatchers/Block/n="1",i=~"1.+",j="foo"-4 27960350 27264997 -2.49%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!="2",j="foo"-4 136765564 133860724 -2.12%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!~"2.*",j="foo"-4 163714583 159453668 -2.60%
benchmark old allocs new allocs delta
BenchmarkPostingsForMatchers/Block/n="1"-4 6 6 +0.00%
BenchmarkPostingsForMatchers/Block/n="1",j="foo"-4 11 11 +0.00%
BenchmarkPostingsForMatchers/Block/j="foo",n="1"-4 11 11 +0.00%
BenchmarkPostingsForMatchers/Block/n="1",j!="foo"-4 17 15 -11.76%
BenchmarkPostingsForMatchers/Block/i=~".*"-4 100012 12 -99.99%
BenchmarkPostingsForMatchers/Block/i=~".+"-4 200040 100040 -49.99%
BenchmarkPostingsForMatchers/Block/i=~""-4 200045 100045 -49.99%
BenchmarkPostingsForMatchers/Block/i!=""-4 200041 100041 -49.99%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",j="foo"-4 100017 17 -99.98%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",i!="2",j="foo"-4 100023 23 -99.98%
BenchmarkPostingsForMatchers/Block/n="1",i!=""-4 200046 100046 -49.99%
BenchmarkPostingsForMatchers/Block/n="1",i!="",j="foo"-4 200050 100050 -49.99%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",j="foo"-4 200049 100049 -49.99%
BenchmarkPostingsForMatchers/Block/n="1",i=~"1.+",j="foo"-4 111150 11150 -89.97%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!="2",j="foo"-4 200055 100055 -49.99%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!~"2.*",j="foo"-4 311238 111238 -64.26%
benchmark old bytes new bytes delta
BenchmarkPostingsForMatchers/Block/n="1"-4 296 296 +0.00%
BenchmarkPostingsForMatchers/Block/n="1",j="foo"-4 424 424 +0.00%
BenchmarkPostingsForMatchers/Block/j="foo",n="1"-4 424 424 +0.00%
BenchmarkPostingsForMatchers/Block/n="1",j!="foo"-4 552 1544 +179.71%
BenchmarkPostingsForMatchers/Block/i=~".*"-4 1600482 1606125 +0.35%
BenchmarkPostingsForMatchers/Block/i=~".+"-4 17259065 17264709 +0.03%
BenchmarkPostingsForMatchers/Block/i=~""-4 17259150 17264780 +0.03%
BenchmarkPostingsForMatchers/Block/i!=""-4 17259048 17264680 +0.03%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",j="foo"-4 1600610 1606242 +0.35%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",i!="2",j="foo"-4 1600813 1606434 +0.35%
BenchmarkPostingsForMatchers/Block/n="1",i!=""-4 17259176 17264808 +0.03%
BenchmarkPostingsForMatchers/Block/n="1",i!="",j="foo"-4 17259304 17264936 +0.03%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",j="foo"-4 17259333 17264965 +0.03%
BenchmarkPostingsForMatchers/Block/n="1",i=~"1.+",j="foo"-4 3142628 3148262 +0.18%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!="2",j="foo"-4 17259509 17265141 +0.03%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!~"2.*",j="foo"-4 20405680 20416944 +0.06%
However overall Select performance is down and involves more allocs, due to
having to do more than a simple map lookup to resolve a symbol and that all the strings
returned are allocated:
benchmark old ns/op new ns/op delta
BenchmarkQuerierSelect/Block/1of1000000-4 506092636 862678244 +70.46%
BenchmarkQuerierSelect/Block/10of1000000-4 505638968 860917636 +70.26%
BenchmarkQuerierSelect/Block/100of1000000-4 505229450 882150048 +74.60%
BenchmarkQuerierSelect/Block/1000of1000000-4 515905414 862241115 +67.13%
BenchmarkQuerierSelect/Block/10000of1000000-4 516785354 874841110 +69.29%
BenchmarkQuerierSelect/Block/100000of1000000-4 540742808 907030187 +67.74%
BenchmarkQuerierSelect/Block/1000000of1000000-4 815224288 1181236903 +44.90%
benchmark old allocs new allocs delta
BenchmarkQuerierSelect/Block/1of1000000-4 4000020 6000020 +50.00%
BenchmarkQuerierSelect/Block/10of1000000-4 4000038 6000038 +50.00%
BenchmarkQuerierSelect/Block/100of1000000-4 4000218 6000218 +50.00%
BenchmarkQuerierSelect/Block/1000of1000000-4 4002018 6002018 +49.97%
BenchmarkQuerierSelect/Block/10000of1000000-4 4020018 6020018 +49.75%
BenchmarkQuerierSelect/Block/100000of1000000-4 4200018 6200018 +47.62%
BenchmarkQuerierSelect/Block/1000000of1000000-4 6000018 8000019 +33.33%
benchmark old bytes new bytes delta
BenchmarkQuerierSelect/Block/1of1000000-4 176001468 227201476 +29.09%
BenchmarkQuerierSelect/Block/10of1000000-4 176002620 227202628 +29.09%
BenchmarkQuerierSelect/Block/100of1000000-4 176014140 227214148 +29.09%
BenchmarkQuerierSelect/Block/1000of1000000-4 176129340 227329348 +29.07%
BenchmarkQuerierSelect/Block/10000of1000000-4 177281340 228481348 +28.88%
BenchmarkQuerierSelect/Block/100000of1000000-4 188801340 240001348 +27.12%
BenchmarkQuerierSelect/Block/1000000of1000000-4 304001340 355201616 +16.84%
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
With recent speed improvements to populate block,
the cancellation test now fails regularly on CI.
Use contexts to get the index writer to shut down
much faster, and that allows us to make the cancellation
test faster too.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
Rather than building up a 2nd copy of all the posting
tables, construct it from the data we've already written
to disk. This takes more time, but saves memory.
Current benchmark numbers have this as slightly faster, but that's
likely due to the synthetic data not having many label names.
Memory usage is roughly halved for the relevant bits.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
Rather than keeping the offset of each postings list, instead
keep the nth offset of the offset of the posting list. As postings
list offsets have always been sorted, we can then get to the closest
entry before the one we want an iterate forwards.
I haven't done much tuning on the 32 number, it was chosen to try
not to read through more than a 4k page of data.
Switch to a bulk interface for fetching postings. Use it to avoid having
to re-read parts of the posting offset table when querying lots of it.
For a index with what BenchmarkHeadPostingForMatchers uses RAM
for r.postings drops from 3.79MB to 80.19kB or about 48x.
Bytes allocated go down by 30%, and suprisingly CPU usage drops by
4-6% for typical queries too.
benchmark old ns/op new ns/op delta
BenchmarkPostingsForMatchers/Block/n="1"-4 35231 36673 +4.09%
BenchmarkPostingsForMatchers/Block/n="1",j="foo"-4 563380 540627 -4.04%
BenchmarkPostingsForMatchers/Block/j="foo",n="1"-4 536782 534186 -0.48%
BenchmarkPostingsForMatchers/Block/n="1",j!="foo"-4 533990 541550 +1.42%
BenchmarkPostingsForMatchers/Block/i=~".*"-4 113374598 117969608 +4.05%
BenchmarkPostingsForMatchers/Block/i=~".+"-4 146329884 139651442 -4.56%
BenchmarkPostingsForMatchers/Block/i=~""-4 50346510 44961127 -10.70%
BenchmarkPostingsForMatchers/Block/i!=""-4 41261550 35356165 -14.31%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",j="foo"-4 112544418 116904010 +3.87%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",i!="2",j="foo"-4 112487086 116864918 +3.89%
BenchmarkPostingsForMatchers/Block/n="1",i!=""-4 41094758 35457904 -13.72%
BenchmarkPostingsForMatchers/Block/n="1",i!="",j="foo"-4 41906372 36151473 -13.73%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",j="foo"-4 147262414 140424800 -4.64%
BenchmarkPostingsForMatchers/Block/n="1",i=~"1.+",j="foo"-4 28615629 27872072 -2.60%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!="2",j="foo"-4 147117177 140462403 -4.52%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!~"2.*",j="foo"-4 175096826 167902298 -4.11%
benchmark old allocs new allocs delta
BenchmarkPostingsForMatchers/Block/n="1"-4 4 6 +50.00%
BenchmarkPostingsForMatchers/Block/n="1",j="foo"-4 7 11 +57.14%
BenchmarkPostingsForMatchers/Block/j="foo",n="1"-4 7 11 +57.14%
BenchmarkPostingsForMatchers/Block/n="1",j!="foo"-4 15 17 +13.33%
BenchmarkPostingsForMatchers/Block/i=~".*"-4 100010 100012 +0.00%
BenchmarkPostingsForMatchers/Block/i=~".+"-4 200069 200040 -0.01%
BenchmarkPostingsForMatchers/Block/i=~""-4 200072 200045 -0.01%
BenchmarkPostingsForMatchers/Block/i!=""-4 200070 200041 -0.01%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",j="foo"-4 100013 100017 +0.00%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",i!="2",j="foo"-4 100017 100023 +0.01%
BenchmarkPostingsForMatchers/Block/n="1",i!=""-4 200073 200046 -0.01%
BenchmarkPostingsForMatchers/Block/n="1",i!="",j="foo"-4 200075 200050 -0.01%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",j="foo"-4 200074 200049 -0.01%
BenchmarkPostingsForMatchers/Block/n="1",i=~"1.+",j="foo"-4 111165 111150 -0.01%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!="2",j="foo"-4 200078 200055 -0.01%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!~"2.*",j="foo"-4 311282 311238 -0.01%
benchmark old bytes new bytes delta
BenchmarkPostingsForMatchers/Block/n="1"-4 264 296 +12.12%
BenchmarkPostingsForMatchers/Block/n="1",j="foo"-4 360 424 +17.78%
BenchmarkPostingsForMatchers/Block/j="foo",n="1"-4 360 424 +17.78%
BenchmarkPostingsForMatchers/Block/n="1",j!="foo"-4 520 552 +6.15%
BenchmarkPostingsForMatchers/Block/i=~".*"-4 1600461 1600482 +0.00%
BenchmarkPostingsForMatchers/Block/i=~".+"-4 24900801 17259077 -30.69%
BenchmarkPostingsForMatchers/Block/i=~""-4 24900836 17259151 -30.69%
BenchmarkPostingsForMatchers/Block/i!=""-4 24900760 17259048 -30.69%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",j="foo"-4 1600557 1600621 +0.00%
BenchmarkPostingsForMatchers/Block/n="1",i=~".*",i!="2",j="foo"-4 1600717 1600813 +0.01%
BenchmarkPostingsForMatchers/Block/n="1",i!=""-4 24900856 17259176 -30.69%
BenchmarkPostingsForMatchers/Block/n="1",i!="",j="foo"-4 24900952 17259304 -30.69%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",j="foo"-4 24900993 17259333 -30.69%
BenchmarkPostingsForMatchers/Block/n="1",i=~"1.+",j="foo"-4 3788311 3142630 -17.04%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!="2",j="foo"-4 24901137 17259509 -30.69%
BenchmarkPostingsForMatchers/Block/n="1",i=~".+",i!~"2.*",j="foo"-4 28693086 20405680 -28.88%
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
Arve Knudsen